• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.341-345
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• 2021

Known to improve resistance to chloride ingress, blast furnace slag is a widely used supplementary cementitious material. However, a detailed characterization of cements blended with slag exposed to seawater remains unavailable. This study employs thermodynamic modeling as a toolkit for assessing the long-term phase evolution of slag cement in seawater. The modeling result shows that slag incorporation leads to the formation of phases that are less prone to structural alteration in seawater. Formation of more ettringite is expected to induce expansion in both plain and blended cements, while brucite is unstable in the blended systems. Despite this, the porosity is expected to increase in the blended cements, and aluminate hydrates with a higher chloride binding capacity are more abundant in the blended cements. The results suggest that the use of slag in concrete improves the durability performance of concrete in marine environments.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.47-58
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• 2018

Recently, remote sensing video applications have become increasingly important in many wireless networks. Distributed compressive video sensing (DCVS) framework in these applications has been studied to reduce encoding complexity and to simultaneously capture and compress video data. Specially, a motion compensated block compressed sensing with smoothed projected Landweber (MC-BCS-SPL) has been actively researched for one useful algorithm of DCVS schemes, However, conventional MC-BCS-SPL schemes do not provide good visual qualities in reconstructed Wyner-Ziv (WZ) frames. In this paper, the conventional schemes of MC-BCS-SPL are described and then upgraded to provide better visual qualities in WZ frames by introducing reliability estimate between adjacent key frames and by constructing efficiently motion-compensated interpolated frames. Through experimental results, it is shown that the proposed algorithm is effective in providing better visual qualities than conventional algorithm.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.47-53
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• 2023

With the advent of the 4th Industrial Revolution, diverse and a large amount of data has been accumulated now. The agricultural community has also collected environmental data that affects the growth of crops in smart farms or open fields with sensors. Environmental data has different features depending on where and when they are measured. Studies have been conducted using collected agricultural data to predict growth and yield with statistics and artificial intelligence. The results of these studies vary greatly depending on the data on which they are based. So, studies to enhance data quality have also been continuously conducted for performance improvement. A lot of data is required for high performance, but if there are outlier or missing values in the data, it can greatly affect the results even if the amount is sufficient. So, adjustment of outlier and missing values is essential in the data preprocessing. Therefore, this paper integrates data collected from actual farms and proposes a adjustment system for outlier and missing values based on it.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of the Korean Vacuum Society
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• v.10 no.4
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• pp.403-410
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• 2001

Acceleration in integration density and speed performance of ULSI circuits require miniaturization of CMOS and interconnections as well as higher current density capabilities for transistors. A leading candidate to substitute Al-alloy is Cu, which has lower resistivity and higher melting point. So we can expect much higher electromigration resistance. In this paper, we are going to explain the major features of EM for MOCVD Cu according to variant conditions. We compared the life time and activation energy of MOCVD Cu with those of I-beam Cu and AA in the same conditions. The electromigration experiments were performed with Cu/Al/TiN multilayer. Experimental results shows that the deposition rate and electromigration characteristics of Cu thin film were improved by the Al interlayer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.649-652
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• 2015

Previous marking policy for the AF service of TCP traffic in the Diffserv network have no sufficient consideration on the effect of RTT and target rate. In this paper, in order to improve fairness Index by the effect RTT difference of TCP traffic, we propose the modified TSW3CDM(Time Sliding Window Three Color Dynamic Marker) based on average transfer rate estimation and the flow state. The proposed algorithm is dynamic marking policy that do allocate band width in proportion to transmission rate. To evaluate the performance of the proposed algorithm, We accomplished a computer simulation using NS-2. From simulation results, the proposed TSW3CDM algorithm improves fairness index by comparison with TSW3CM.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.103-103
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• 2020

Drying Stream Assessment Tool and Water Flow Tracking (DrySAT-WFT)은 하천건천화 평가 및 예측을 위해 개발된 물수지 기반의 분포형 수문모형이다. 그러나 물수지 모형의 특성상 토양층 사이를 이동하는 수직적인 물의 거동은 파악하기 용이하나, 하천 및 지표를 따라 이동하는 물의 수평적인 거동 추적에는 한계가 있다. 본 연구에서는 DrySAT-WFT 모형에 댐·보 방류량을 고려한 하도 갈수량 추적 알고리즘을 적용하여 유출 모의 성능을 개선하고, 개선된 유출 모형을 금강 유역(9,915.5 ㎢)에 적용하여 건천화 원인 추적 및 평가를 수행하였다. 하천건천화 원인 추적을 위한 영향요소로 1976년부터 2015년까지 구축한 산림높이, 도로망, 지하수 이용량, 토지이용, 토심, 기상 자료를 활용하였다. 건천화 영향요소를 적용하기 전 기상자료만을 활용해 모의한 유출결과를 기준 시나리오로 설정하고 댐·보 지점을 대상으로 검보정을 진행하였다. 이후 각 건천화 영향요소를 적용한 유출 시나리오별 유량의 감소 비율과 건천화 기여 비율을 산정하여 영향평가를 수행하였다.

• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.409-423
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• 2015

In this study the very short-term rainfall forecasting and storm water forecasting using the weather radar data were implemented in an urban stream basin. As forecasting time increasing, the very short-term rainfall forecasting results show that the correlation coefficient was decreased and the root mean square error was increased and then the forecasting model accuracy was decreased. However, as a result of the correlation coefficient up to 60-minute forecasting time is maintained 0.5 or higher was obtained. As a result of storm water forecasting in an urban area, the reduction in peak flow and outflow volume with increasing forecasting time occurs, the peak time was analyzed that relatively matched. In the application of storm water forecasting by radar rainfall forecast, the errors has occurred that we determined some of the external factors. In the future, we believed to be necessary to perform that the continuous algorithm improvement such as simulation of rapid generation and disappearance phenomenon by precipitation echo, the improvement of extreme rainfall forecasting in urban areas, and the rainfall-runoff model parameter optimizations. The results of this study, not only urban stream basin, but also we obtained the observed data, and expand the real-time flood alarm system over the ungaged basins. In addition, it is possible to take advantage of development of as multi-sensor based very short-term rainfall forecasting technology.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.249-255
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• 2019

Type 2 diabetes mellitus(T2DM) is included in metabolic disorders characterized by hyperglycemia, which causes many complications, and requires long-term treatment resulting in massive medical expenses each year. There have been many studies to solve this problem, but the existing studies have not been accurate by learning and predicting the data at specific time point. Thus, this study proposed a model using RNN to increase the accuracy of prediction of T2DM. This work propose a T2DM prediction model based on Korean Genome and Epidemiology study(Ansan, Anseong Korea). We trained all of the data over time to create prediction model of diabetes. To verify the results of the prediction model, we compared the accuracy with the existing machine learning methods, LR, k-NN, and SVM. Proposed prediction model accuracy was 0.92 and the AUC was 0.92, which were higher than the other. Therefore predicting the onset of T2DM by using the proposed diabetes prediction model in this study, it could lead to healthier lifestyle and hyperglycemic control resulting in lower risk of diabetes by alerted diabetes occurrence.